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2. Dates Fruit Disease Recognition using Machine Learning

Author

Brahim, Ghassen Ben, Alghazo, Jaafar, Latif, Ghazanfar, and Alnujaidi, Khalid

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Many countries such as Saudi Arabia, Morocco and Tunisia are among the top exporters and consumers of palm date fruits. Date fruit production plays a major role in the economies of the date fruit exporting countries. Date fruits are susceptible to disease just like any fruit and early detection and intervention can end up saving the produce. However, with the vast farming lands, it is nearly impossible for farmers to observe date trees on a frequent basis for early disease detection. In addition, even with human observation the process is prone to human error and increases the date fruit cost. With the recent advances in computer vision, machine learning, drone technology, and other technologies; an integrated solution can be proposed for the automatic detection of date fruit disease. In this paper, a hybrid features based method with the standard classifiers is proposed based on the extraction of L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features for the early detection and classification of date fruit disease. A dataset was developed for this work consisting of 871 images divided into the following classes; Healthy date, Initial stage of disease, Malnourished date, and Parasite infected. The extracted features were input to common classifiers such as the Random Forest (RF), Multilayer Perceptron (MLP), Na\"ive Bayes (NB), and Fuzzy Decision Trees (FDT). The highest average accuracy was achieved when combining the L*a*b, Statistical, and DWT Features.

Published
2023

5. Deep Learning-based Arabic Sign Recognition System for Automated Communication with Hearing Impaired Individuals.

Author

Alghazo, Runna, Latif, Ghazanfar, Mohammad, Nazeeruddin, Alghazo, Jaafar, and Pilotti, Maura A. E.

Abstract

Arabic Sign Language (ArSL) is used by individuals who are hard of hearing or deaf in Arab countries, as well as others around the world who use it for religious purposes. for the need for automated systems to facilitate the learning and communication of ArSL is therefore significant. Such systems would allow people to learn Arabic Sign Language and use it to communicate among themselves and with the surrounding community. This paper presents the development of an automatic recognition system capable of accurately identifying Arabic signs through hand gestures. In this paper, two Residual Network (ResNet) Configurations, Version 1 (V1) and Version 2 (V2), are proposed and detailed. The proposed ResNet V1 achieved an average accuracy of 98.83%, while ResNet V2 achieved an average accuracy of 98.84%. The results described in this paper far exceed those reported in the extant literature. The high accuracy of the proposed system shows the potential for integrating the system with education tools and assistive technologies for people with special needs. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Arabic Braille Numeral Recognition Using Convolutional Neural Networks

Author

Alufaisan, Shurouq, Albur, Wafa, Alsedrah, Shaikha, Latif, Ghazanfar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Bindhu, V., editor, Tavares, João Manuel R. S., editor, Boulogeorgos, Alexandros-Apostolos A., editor, and Vuppalapati, Chandrasekar, editor

Published
2021
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7. Lung Cancer Detection from LDCT Images Using Deep Convolutional Neural Networks

Author

Alghamdi, Shahad, Alabkari, Mariam, Aljishi, Fatima, Latif, Ghazanfar, Bashar, Abul, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Bindhu, V., editor, Tavares, João Manuel R. S., editor, Boulogeorgos, Alexandros-Apostolos A., editor, and Vuppalapati, Chandrasekar, editor

Published
2021
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8. CNN-Based Alphabet Identification and Sorting Robotic Arm

Author

Al-Faraj, Saleh, Al-Bahrani, Mustafa, Al-Ghamdi, Saeed, Rafie, Marwan, Bashar, Abul, Latif, Ghazanfar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Bindhu, V., editor, Tavares, João Manuel R. S., editor, Boulogeorgos, Alexandros-Apostolos A., editor, and Vuppalapati, Chandrasekar, editor

Published
2021
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9. IoT Cloud Based Rx Healthcare Expert System

Author

Latif, Ghazanfar, Alghazo, Jaafar, Celebi, Emre, Series Editor, Chen, Jingdong, Series Editor, Gopi, E. S., Series Editor, Neustein, Amy, Series Editor, Poor, H. Vincent, Series Editor, and Tanwar, Sudeep, editor

Published
2021
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14. Deep convolutional neural network (CNN) model optimization techniques--Review for medical imaging.

Author

Latif, Ghazanfar, Alghazo, Jaafar, Khan, Majid Ali, Ben Brahim, Ghassen, Fawagreh, Khaled, and Mohammad, Nazeeruddin

Subjects
CONVOLUTIONAL neural networks, IMAGE recognition (Computer vision), COMPUTER-assisted image analysis (Medicine), ARTIFICIAL intelligence, DIAGNOSTIC imaging
Abstract

The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase. [ABSTRACT FROM AUTHOR]

Published
2024
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15. A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images.

Author

Latif, Ghazanfar, Alghazo, Jaafar, Mohammad, Nazeeruddin, Abdelhamid, Sherif E., Brahim, Ghassen Ben, and Amjad, Kashif

Subjects
PATIENT-professional relations, MEDICAL personnel as patients, MEDICAL records, DIAGNOSTIC imaging, DATA security, DEEP learning
Abstract

Medical health records hold personal medical information and should only be accessed by authorized medical personnel or concerned patients. The importance of medical health records privacy is increasing as these records are shared in cloud environments. In this paper, we propose an enhanced system for securing patient data (Medical Health Records) embedded in multiple medical images in fragments for secure transmission and public sharing on the cloud or other environments. To protect the patient's privacy, Medical Records are first encrypted, and then the ciphertext is broken into several fragments based on the number of multimodal medical images of a patient. A key generator randomly selects medical images from the multimodal image data to embed the encrypted patient health record segment using a modified least significant bit embedding process. The proposed technique enables an extra layer of security as even if files fall into the wrong hands and a fragment of the file is decrypted, it will not present any understandable information until all fragments from other medical images are extracted and combined in the correct order. The experiments are performed using multimodal 3255 MRI scans of 21 patients. The robustness of the proposed method was measured using different metrics such as PSNR, MSE, and SSIM. The results show that the proposed system is robust and that image quality is also maintained. To further study the stego image quality, a deep learning-based classification was applied to the images, and the results show that the diagnosis using stego medical images and performance remains unaffected even after embedding the encrypted data. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Efficient Routing in Geographic and Opportunistic Routing for Underwater WSNs

Author

Latif, Ghazanfar, Javaid, Nadeem, Khan, Aasma, Fatima, Aisha, Jatta, Landing, Khan, Wahab, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Xhafa, Fatos, editor, Javaid, Nadeem, editor, and Enokido, Tomoya, editor

Published
2019
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17. An Efficient Routing Protocol via Depth Adjustment and Energy Gradation in Underwater Wireless Sensor Networks

Author

Latif, Ghazanfar, Javaid, Nadeem, Iqbal, Arshad, Ahmad, Javed, Jabbar, Ather Abdul, Imran, Muhammad, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Xhafa, Fatos, editor, Javaid, Nadeem, editor, and Enokido, Tomoya, editor

Published
2019
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